Summary of Automating Data Annotation Under Strategic Human Agents: Risks and Potential Solutions, by Tian Xie et al.
Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions
by Tian Xie, Xueru Zhang
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study investigates the long-term effects of machine learning models retrained with self-annotated samples, which incorporate human strategic responses. The research formalizes the interactions between strategic agents and the model, analyzing how they evolve under dynamic interactions. The findings suggest that agents are more likely to receive positive decisions as the model is retrained, but the proportion of agents with positive labels may decrease over time. To stabilize the dynamics, a refined retraining process is proposed. Additionally, the study explores how algorithmic fairness can be affected by these retraining processes and finds that enforcing common fairness constraints at every round may not benefit the disadvantaged group in the long run. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are used to make important decisions about people, but they need to adapt quickly to changing circumstances. One way to do this is to have the model itself label new data, which can be helpful when it’s hard or impossible to get human-labeled data. This study looks at what happens when machine learning models are retrained with self-annotated samples that include human responses. The research shows that as the model gets better, people are more likely to receive positive outcomes, but the overall proportion of people getting positive labels might actually go down over time. To fix this, a new way to retrain the model is proposed. The study also explores how fairness can be affected by these retraining processes and finds that enforcing fairness at every step may not always help those who need it most. |
Keywords
» Artificial intelligence » Machine learning
